Appetite and eating are not only driven by energy needs, but also by extrinsic (environmental) factors unrelated to energy balance. Environmental signals, including learned cues, can override homeostatic signals to stimulate eating in sated states, or inhibit eating in states of hunger. The long-term goal of the proposed research is to define the functional organization of the underlying neural circuits. We use a rodent model for cue-induced eating, in which an environmental cue previously paired with food when an animal was hungry can initiate eating in sated rats. The enhanced eating is a consequence of motivational properties acquired by an otherwise neutral signal through associative learning. The model provides a framework to study how environmental cues are integrated with homeostatic signals within functional forebrain networks, and how these networks are modulated by experience. The proposed research builds on evidence that the network components include areas in the forebrain, amygdala and prefrontal cortex critical for associative learning and decision-making, and the lateral hypothalamus, which is an integrator for feeding, reward and motivation. There are two independent, interrelated specific aims that collectively investigate the neural systems through which signals from the environment control feeding behavior. The first aim is to define the role of the amygdala, and prefrontal cortex subsystems in learned cue-induced feeding, and to identify the hypothalamic substrates that allow learned cue integration with the homeostatic system. The second aim is to map network plasticity that supports learning underling cue-food pairing. The proposed goals will be accomplished with a combination of behavioral and neural systems analysis that include selective neurotoxic lesions, anatomical pathway tracing, and imaging with immediate early genes (IEGs). The IEGs methods will include mapping of neuronal responses to a single event (c-fos protein induction), and mapping of neuronal coincident activation by two events that are temporally arranged (Arc/Arg3.1 mRNA induction). The work under the research plan in this application will extend our understanding of the brain circuitry that allows learning to modulate feeding behavior. The results will be also informative for control of appetite in humans including maladaptive environmental influences that could contribute to overeating and obesity.